Abstract
Mining sub-event relations of major events is an important research problem, which is useful for building event taxonomy, event knowledge base construction, and natural language understanding. To advance the study of this problem, this paper presents a novel dataset called SeRI (Sub-event Relation Inference). SeRI includes 3,917 event articles from English Wikipedia and the annotations of their sub-events. It can be used for training or evaluating a model that mines sub-event relation from encyclopedia-style texts. Based on this dataset, we formally define the task of sub-event relation inference from an encyclopedia, propose an experimental setting and evaluation metrics and evaluate some baseline approaches’ performance on this dataset.
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Notes
- 1.
Event articles refer to the articles that describe a major event in Wikipedia, like Fig. 1.
- 2.
Please contact the first author to request the access to the dataset.
- 3.
To distinguish from a pair which is denoted as \(\langle e_i,e_j \rangle \) for which we do not consider the order of items, we say a pair instance denoted as (\(e_i\),\(e_j\))) when we consider the order of items in a pair.
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Acknowledgments
The research work is supported by the National Science Foundation of China under Grant No. 61772040. The contact author is Zhifang Sui.
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Ge, T., Cui, L., Chang, B., Sui, Z., Wei, F., Zhou, M. (2018). SeRI: A Dataset for Sub-event Relation Inference from an Encyclopedia. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_23
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